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Health monitoring of Mechanical drives had been a challenging task for the researchers, to estimate the fault severity for necessary corrective actions. Signal processing techniques made the task of finding faults quickly in the easiest way, to predict machine condition. Steps of preventive maintenance would always help in reducing the machine down time. Vibrations in mechanical systems, found to be the most common source for degrading the performance, and uncontrollable vibrations usually lead to catastrophic situations. Hence regulating machine vibrations would solve majority of the problems and prevents the state of being abnormal. In this context an empirical mode decomposition technique is utilised to study the vibration severity of a spur gearbox. This method decomposes a complex raw signal into a finite number of Intrinsic mode function (IMFs), based on the local characteristic time scale. These IMFs divulge the intrinsic oscillation modes entrenched in the signal. This enables the fault diagnosis of gears and to take essential corrective action. The present study includes acquisition of vibration signals, decomposition into IMF’s, extraction of respective statistical parameters and analysis for the quantification of various faults. The work also demonstrates the effectiveness of the processed signal over the raw signals of vibration.

Application of EMD based statistical parameters for the prediction of fault severity in a spur gear through vibration signals